The dense deployment of base stations (BSs), which is necessary to satisfy the high demand of traffic, is causing enormous energy consumption with more challenging operational cost. Therefore all stockholders of wireless market possess keen interest for making improvement in energy efficiency at the network level and are putting a large research effort for finding innovative solutions. Most of the pioneering works have shown that mobile networks have a strong potential for energy savings. Most of the works done in literature, have emphasized on reducing energy consumption at
the user end, so that the battery life of mobile terminals can be increased [25]. How- ever, it has been reported in many studies that, state of the art BSs, also known as eNodeBs in LTE networks, are the major source of energy consumption, consuming approximately 60 − 80% of the total energy of a cellular network [26]. Fig.1-3 [3] also shows that BSs are the major source of energy consumption of a wireless net- work. This is mainly because of the always-active operation of current systems. This always-active mode offers full-time coverage but fails to adapt energy consumption to traffic load variations. Therefore designing energy efficient BSs has become the most important issue for any green communication networks. Hence, operators, vendors and researchers are collaborating to propose innovative technologies and algorithms to improve energy efficiency in the BSs. Researchers in many different papers have proposed various distinctive approaches to reduce energy consumptions in BSs [27] which can be summed up in the categories as shown in Fig.1-7.
The first two approaches from the above list involves architectural changes as well as the cost of purchasing, replacing, and installing new equipments. These costs also include the expenditure involved in manpower, transportation as well as associated energy and direct cost. On contrary, rest of the three approaches that are applicable on the operating protocols of the system are less expensive and easily implementable as they do not require changes to current network architecture. In this thesis we limit our research scope to the last challenge area from the above list, where we identify literature gaps and formulate our research questions accordingly.
1.7.1
Sleep Mode Techniques in The Base Station
As discussed above, as sleep mode implementation in the BS neither require upgrade of equipments nor any hardware replacement so they incur low implementation cost, hence is much preferable energy efficient approach by the operators and vendors. The total power consumption of a BS is composed of fixed power consumption, which does not depend on the traffic load and traffic dependent power consumption, which varies with the traffic variation. As presented in paper [4], the fixed part, including air con- ditioning and power supply, consumes around one fourth of total energy consumption,
Approaches to reduce energy consumption of a BS 1. Updating the hardware components. 2. Adopting renewable energy resources. 3. Optimizing energy efficiency of the radio transmission process. 4. Implementation and Deployment of low power consuming small cells. 5. Switching off some of the components of a BS selectively.
which is wasted when there is no traffic to be served by the BS. This unnecessary energy consumptions can be avoided by adopting sleep mode mechanisms in the BSs. The sleep mode approaches generally involve switching off the entire BS or certain elements including but not limited to power amplifiers, cooling equipment or the sig- nal processing unit [28] in low traffic condition. As already mentioned BSs are the highest energy consuming part in the cellular networks. Moreover, dense deployments of BSs lead to small coverage area and more random traffic patterns for individual BS, which make sleep mode operations more desirable. The following challenges in implementing sleep mode in BSs have been identified in these studies which need more attention from the researchers:
∙ Implementing sleep mode might have negative impact on QoS in the network because of decreasing coverage and capacity.
∙ The time needed to activate the sleeping BS causes delay in providing service, which causes degradation in service and may cause call drops as well.
∙ BS cooperation is necessary to avoid outage events when the some of the BSs are put in sleep mode.
∙ Hardware components, which remain active during sleep mode, should be char- acterized by a very limited power consumption to avoid energy wastage. ∙ Current 3GPP standard constrains continuous transmission of pilot channels to
guarantee coverage [29].
In order to address the above challenge, some significant works have been dedi- cated to reduce the energy consumption of the BSs by implementing sleep modes in the past few years [9, 30–44]. The main idea of these works is to find the minimum transmission power which ensures QoS in terms of coverage and capacity. J. Peng et. al. in [30] proposed an energy saving approach by switching off some macro BSs under downlink coverage and uplink power constraints. They considered the users’ power constraints to formulate a BS energy consumption minimization problem. they also determined the optimal proportion of sleep macro BSs and transmission power
of active macro BSs and their results showed significant reduction in BSs’ energy consumption while guaranteeing the downlink coverage and user power consumption performance. Saker et al. [34] presented an energy-efficient system selection scheme by spliting the mobile traffic between 2G and 3G systems optimally, which can reduce around 10% of total energy consumption while satisfying QoS requirements. Then they implemented a sleep mode for either 2G or 3G systems. Their results showed that a significant amount of energy can be saved during low or medium traffic con- dition without degrading the QoS. Another study conducted by the the same group of researchers as presented in [38], proposed a generic framework for applying sleep mode to the BSs of mobile cellular networks. Their work was divided in two schemes, firstly they proposed a dynamic scheme where BSs are put to sleep or waken up based on the instantaneous number of users in the cell. The second scheme is a semi-static one where the BSs stay in a particular mode for a certain period of time (tens of minutes or even for hours) in order to minimize frequent transition between the sleep and active mode. These authors also discussed practical issues for sleep mode imple- mentation in BSs in another paper [37]. In this work they proposed a guard period and a hysteresis time between active and sleep mode, in order to to avoid call block- ing when the resources are being activated and to reduce frequent mode transition. Their simulation results showed that both guard period and hysteresis time provide better QoS but reduce the gain in energy efficiency. Also their guard period and hysteresis time do not adapt varying traffic condition, therefore is less suitable for practical implementation. We should also consider the delay caused by the wake up time from sleep mode. The deep sleep mode consumes almost zero power, however can cause significant delay in service due to wake up time from sleep mode, whereas stand-by mode is a lighter sleep mode, where a resource consumes little power but wakes up very quickly. This stand-by mode can be achieved by switching off only the most power consuming part of the BS, such as the power amplifier. We have reviewed more similar works from literature and have presented them in Chapter-2. In the light of these literature reviews, we identify the following literature gaps in this area which are the main focus of our research:
∙ Lack of an sleep mode algorithm which fulfills QOS requirement as well as reduces wake up delay.
∙ Lack of sleep mode scheme offering sleep mode and stand-by mode together in order to reduce energy consumption as well as wake up delay.
Our work presented in Chapter-4, fills the performance gap presented above.